A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering

Du, Yongping; Yao, Changqing; Huo, Shuhua; Liu, Jing-xuan

doi:10.1631/fitee.1601732

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…DL-based methods are also used in RS to handle with sparsity problem by combining user preferences, and side information in hybrid approaches [12,13]. Reducing the dimension of user-item preference matrix with DL-based techniques is also utilized in RS to handle with sparsity [14,15]. Despite the abundance of studies based on DL techniques in single ratingoriented RS, the number of DL-based approaches aiming to handle with sparsity and accuracy issues in MCCF is limited.…”

Section: Related Workmentioning

confidence: 99%

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide their users an efficient way to handle with information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix which constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences over under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multi-criteria recommender systems are an extension of traditional recommender systems that utilize multi-criteria-based user preferences. Multi-criteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be a major problem of such systems. Especially, the similarity-based multi-criteria recommender systems may fail to find similar neighbors to an active user due to the lack of co-rated items among users.Therefore, we propose a new similarity-based multi-criteria collaborative filtering approach based on autoencoders. In order to handle with sparsity, the proposed method extracts non-linear, low-dimensional, dense features from raw and sparse users'/items' preferences. Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multi-criteria recommendation techniques.

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Section: Related Workmentioning

confidence: 99%

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

2021

Turk J Elec Eng & Comp Sci

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“…For example, in [7] Guo et al proposed a novel method called "Merge" to incorporate social trust information, and supplement user preference by merging users' trusted neighbor ratings. In [8] Hu et al integrated time information into collaborative filtering similarity measure in collaborative filtering algorithm, and designed a hybrid personalized random walk algorithm; Yong-ping Du et al [5] proposed item-based RBM, and used deep and multilayer RBM network structure to solve the problem of data sparsity; Sedhain et al [20] generalized matrix algebra framework, and they doesn't need the target user's data when the side information is available ; Jian Wei et al [25] put forward two models on the basis of a framework based on tight-coupling collaborative filtering and the in-depth study into neural network; A. Murat Yagci et al [26] focused on frequent co-occurrence items and proposed SASCF to eliminate the cold start of the system; Su Hongyi et al [22] proposed a new algorithm involving time decay factor in the CF algorithm, and deployed time weights on the MapReduce parallel computing framework ; Xiuju Liu et al [13] presented a new algorithm of CF-ISEGB, and took the influence sets of current e-learning groups into consideration to effectively solve problems caused by sparse data sets.…”

Section: Application Of Improved Collaborative Filtering In the Recom...mentioning

confidence: 99%

Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Chang

Gui

Fan

et al. 2019

INT J COMPUT COMMUN

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Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. In order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (SVM). Different with traditional collaborative filtering, we used SVM to classify commodities into positive and negative feedbacks. Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm. Experiments on Taobao data (a Chinese online shopping website owned by Alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the E-commerce industry.

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“…The item-based collaborative filtering algorithm mainly includes: (1) using the existing user item rating record to calculate the similarity between items;(2) finding the nearest neighbor set of the target item according to the size of the similarity; (3) predicting the target item's rating by using the target user's rating of the nearest neighbor set item, and recommending the item to the user with a high forecast rating [15,16,17]. In order to alleviate data sparseness and scalability problem of the user-based collaborative filtering recommendation algorithm, it chooses to use Slope one algorithm [18].…”

Section: Item-based Collaborative Filtering Algorithmmentioning

confidence: 99%

Field Information Recommendation Based on Context-Aware and Collaborative Filtering Algorithm

Chen

Zhao

2019

Computer and Computing Technologies in Agriculture XI

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Personalized recommendation technology is a valid way to solve the problem of "information overload". In the face the complexity of agricultural field information and problems of farmers' preference prediction accuracy which is not high, this paper proposes a kind of recommendation algorithm based on context-aware and collaborative filtering(CACF). The algorithm constructs the "user-item-context" 3D user interest model which contains the context information. Through calculating context similarity and adopting pre-filtering paradigm, the 3D model is reduced to "user-item"2D model. By computing item similarity, it can predict the item rating and generate recommendations. The CACF was applied on the field information recommendation. The experimental results show that the CACF can accomplish higher recommendation precision and efficiency compared with the traditional User-based collaborative filtering algorithm(UBCF), Slope one algorithm(SLOA).

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A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering

Cited by 18 publications

References 9 publications

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Field Information Recommendation Based on Context-Aware and Collaborative Filtering Algorithm

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